Analysis of categorical breakdown by Carb and bio
library(foreign)
library(magrittr)
library(tidyverse)
library(ggmosaic)
library(janitor)
library(plotly)
myData <- read.dbf("TalamhPrescriptions_forVisualisation.dbf")
head(myData)
## comsub gis_area bio_opt rec_opt sppdiv_opt carb_opt TalamhOpt
## 1 27958W6 2.12 standardmgmt <NA> <NA> NOTHIN_MMAI NOTHIN_MMAI
## 2 71702H1 13.09 CCF <NA> <NA> NOTHIN_MMAI CCF
## 3 35459N2 0.52 bioclass <NA> <NA> RETAIN bioclass
## 4 35472N2 3.39 standardmgmt <NA> <NA> NOTHIN_MMAI NOTHIN_MMAI
## 5 71024L1 1.72 standardmgmt <NA> <NA> NOTHIN_MMAI NOTHIN_MMAI
## 6 66294Q19 2.66 standardmgmt <NA> <NA> NOTHIN_MMAI NOTHIN_MMAI
myData.Report <- myData %>% group_by(bio_opt, carb_opt) %>% summarize( GIS_AREA = sum(gis_area) )
## `summarise()` has grouped output by 'bio_opt'. You can override using the `.groups` argument.
# Overlapping Objectives
myData.Report <- myData.Report %>% mutate(OverLap = "No-Overlap")
myData.Report$OverLap[ as.character(myData.Report$bio_opt)== as.character(myData.Report$carb_opt)] = as.character(myData.Report$bio_opt)[ as.character(myData.Report$bio_opt)== as.character(myData.Report$carb_opt)]
myData.Report$bio_opt <- as.character(myData.Report$bio_opt)
myData.Report$bio_opt[myData.Report$bio_opt %in% c("4YearGreenUp","4YearGreenUp_catch","standardmgmt")] = "NOTHIN_MMAI"
myData.Report$carb_opt <- factor(myData.Report$carb_opt,levels = c("NOTHIN_MMAI","CONV_TO_SNW","RETAIN","CONV_TO_SNW_MINERAL","REWET"))
myData.Report %>% tabyl(bio_opt)
## bio_opt n percent
## bioclass 5 0.16666667
## CCF 5 0.16666667
## NOTHIN_MMAI 14 0.46666667
## RestoreRewild 5 0.16666667
## RETAIN 1 0.03333333
p <- myData.Report %>% mutate( bio_opt = as.character(bio_opt),
carb_opt = as.character(carb_opt)) %>%
ggplot() +
geom_mosaic(aes(weight=GIS_AREA, x = product(carb_opt), fill = bio_opt )) +
scale_fill_manual(values=c("#29ACB1","#285236","#8DBF5A","#1E5631","#19FCA1","#BC21A6")) +
theme( panel.background = element_rect(fill = "white",
colour = "white",
size = 0.5, linetype = "solid"),
panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "white"),
panel.grid.minor = element_line(size = 0.25, linetype = 'solid',
colour = "white"),
axis.text.x = element_text(angle = 45),
axis.text = element_text(face="bold"),
axis.title = element_text(size = 18),
plot.title = element_text(size = 24)) +
xlab("Carbon") + ylab("Bio option") +
ggtitle("Project Talamh")
ggplotly(p,tooltip="text")
myData.Report %>% filter(OverLap=="No-Overlap") %>% mutate( bio_opt = as.character(bio_opt),
carb_opt = as.character(carb_opt)) %>% ggplot( aes(x=bio_opt, y=carb_opt)) +
geom_tile(aes(fill = GIS_AREA))+
scale_fill_distiller(palette = "YlGn") +
labs(title = "HeatMap",
y = "GIS AREA") + theme_bw()
ggplot(data=myData.Report, aes(x=OverLap, y=GIS_AREA, fill=OverLap)) +
geom_bar(stat="identity")
ggplot(data=myData.Report, aes(x=carb_opt, y=GIS_AREA, fill=bio_opt)) +
geom_bar(stat="identity")